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A forecasting tool (API) with examples in curl, R, Python {https://t.co/xKRuexL194} #rstats #DataScience
— R-bloggers (@Rbloggers) May 28, 2021
Deep Learning, Neuronale Netze und TensorFlow 2 in Python https://t.co/C1RBnMmydi
— couponed (@couponed_code) May 28, 2021
💠 #AugmentedReality #TensorFlow #DeepLearning #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #Python #RStats #CloudComputing #Serverless #Linux #Coding #100DaysofCode #100DaysOfMLCode pic.twitter.com/d18tS8MCTc
https://t.co/IRrYaUGGD0#softwaredevelopment #R1D2 #Python #JavaScript #ReactJS #B2B #Coding #programming #bot #WomenWhoCode #100daysofcode #coding #github #cybersecurity #MR #AR #VR #ARcommerce #investment #cryptocurrency #RStats pic.twitter.com/4EJgIN5VOQ
— Spheroid_Krevetka (@ar_spheroid) May 28, 2021
| User | Engagement/Tweet |
|---|---|
| @v_matzek | 2453.0 |
| @kaymwilliamson | 1864.0 |
| @TheToadLady | 1602.5 |
| @kiramhoffman | 1138.0 |
| @adastephenson | 1086.0 |
| @hadleywickham | 952.5 |
| @drhammed | 892.0 |
| @LuukvanderMeer | 778.0 |
| @kimistry8 | 689.0 |
| @kearneymw | 599.4 |
Where Engagement is RT * 2 + Favourite
Relationships in the graph describe replies and quote retweets from the top tweeters that also have the hashtag.
| Word | Count |
|---|---|
| datascience | 21347 |
| python | 18162 |
| 100daysofcode | 15380 |
| machinelearning | 14739 |
| bigdata | 13130 |
| javascript | 12816 |
| analytics | 12533 |
| serverless | 12531 |
| iiot | 11437 |
| programming | 11424 |
---
title: "#rstats Twitter Explorer"
output:
flexdashboard::flex_dashboard:
orientation: rows
vertical_layout: scroll
source_code: embed
theme:
version: 4
bootswatch: yeti
css: styles/main.css
---
```{r load_package, include=FALSE}
devtools::load_all()
```
```{r setup, include=FALSE, cache=TRUE}
library(flexdashboard)
library(rtweet)
library(dplyr)
library(stringr)
library(tidytext)
library(lubridate)
library(echarts4r)
library(DT)
rstats_tweets <- read_twitter_csv("data/rstats_tweets.csv")
count_timeseries <- rstats_tweets %>%
ts_data(by = "hours")
tweets_week <- rstats_tweets %>%
filter(as_datetime(created_at) %within% interval(floor_date(today(), "week"), today()))
tweets_today <- rstats_tweets %>%
filter(created_at == today())
by_hour <- rstats_tweets %>%
group_by(hour = hour(created_at)) %>%
summarise(count = n()) %>%
ungroup()
number_of_unique_tweets <- get_unique_value(rstats_tweets, text)
number_of_unique_tweets_today <-
get_unique_value(tweets_today, text)
number_of_tweeters_today <- get_unique_value(tweets_today, user_id)
number_of_likes <- rstats_tweets %>%
pull(favorite_count) %>%
sum()
top_tweeters <- rstats_tweets %>%
group_by(user_id, screen_name, profile_url, profile_image_url) %>%
summarize(engagement = (sum(retweet_count) * 2 + sum(favorite_count)) / n()) %>%
ungroup() %>%
slice_max(engagement, n = 10, with_ties = FALSE)
top_tweeters_format <- top_tweeters %>%
mutate(
profile_url = stringr::str_glue("https://twitter.com/{screen_name}"),
screen_name = stringr::str_glue('@{screen_name}'),
engagement = formattable::color_bar("#a3c1e0", formattable::proportion)(engagement)
) %>%
select(screen_name, engagement)
top_hashtags <- rstats_tweets %>%
tidyr::separate_rows(hashtags, sep = " ") %>%
count(hashtags) %>%
filter(!(hashtags %in% c("rstats", "RStats"))) %>%
slice_max(n, n = 10, with_ties = FALSE) %>%
mutate(
number = formattable::color_bar("plum", formattable::proportion)(n),
hashtag = stringr::str_glue(
'#{hashtags}'
),
) %>%
select(hashtag, number)
word_banlist <- c("t.co", "https", "rstats")
top_words <- rstats_tweets %>%
select(text) %>%
unnest_tokens(word, text) %>%
anti_join(stop_words) %>%
filter(!(word %in% word_banlist)) %>%
filter(nchar(word) >= 4) %>%
count(word, sort = TRUE) %>%
slice_max(n, n = 10, with_ties = FALSE) %>% mutate(number = formattable::color_bar("peachpuff", formattable::proportion)(n)) %>%
select(word, number)
top_co_hashtags <- rstats_tweets %>%
unnest_tokens(bigram, hashtags, token = "ngrams", n = 2) %>%
tidyr::separate(bigram, c("word1", "word2"), sep = " ") %>%
filter(!word1 %in% c(stop_words$word, word_banlist)) %>%
filter(!word2 %in% c(stop_words$word, word_banlist)) %>%
count(word1, word2, sort = TRUE) %>%
filter(!is.na(word1) & !is.na(word2)) %>%
slice_max(n, n = 100, with_ties = FALSE)
```
Home
====
Row
-----------------------------------------------------------------------
### Tweets Today
```{r tweets_today}
valueBox(number_of_unique_tweets_today, icon = "fa-comment-alt", color = "plum")
```
### Tweeters Today
```{r tweeters_today}
valueBox(number_of_tweeters_today, icon = "fa-user", color = "peachpuff")
```
### #rstats Likes
```{r likes}
valueBox(number_of_likes, icon = "fa-heart", color = "palevioletred")
```
### #rstats Tweets
```{r unique_tweets}
valueBox(number_of_unique_tweets, icon = "fa-comments", color = "mediumorchid")
```
Row {.tabset .tabset-fade}
-----------------------------------------------------------------------
### Tweet volume
```{r tweet_volume}
this_month <- floor_date(today(), "month")
count_timeseries %>%
e_charts(time) %>%
e_line(n, name = "# of tweets", smooth = TRUE, legend = FALSE) %>%
e_x_axis(
type = "time",
formatter = htmlwidgets::JS(
"function(value){
let date = new Date(value);
label = `${date.getDate()}-${(parseInt(date.getMonth()) + 1)}-${date.getFullYear()}`;
return label;
}"
)
) %>%
e_axis_labels(y = "Tweets") %>%
e_theme("westeros") %>%
e_tooltip(trigger = "axis", formatter = htmlwidgets::JS("
function(params) {
let date = new Date(params[0].value[0])
let options = { year: 'numeric', month: 'short', day: 'numeric', hour: 'numeric'}
let title = `${date.toLocaleDateString('en-US', options=options)}`
let num = `${params[0].value[1]} tweets`
return(`${title}${num}`);
}")) %>%
e_datazoom(type = "slider") %>%
e_zoom(
dataZoomIndex = 0,
start = 70,
end = 100
) %>%
e_zoom(
dataZoomIndex = 0,
startValue = today() - 7,
endValue = today(),
btn = "weekBtn"
) %>%
e_zoom(
dataZoomIndex = 0,
startValue = this_month,
endValue = today(),
btn = "monthBtn"
) %>%
e_button(
id = "weekBtn",
position = "top",
class = "btn btn-primary btn-sm",
"This Week"
) %>%
e_button(
id = "monthBtn",
position = "top",
class = "btn btn-primary btn-sm",
"This Month"
)
```
### Tweets by Hour of Day
```{r tweets_by_hour}
by_hour %>%
e_charts(hour) %>%
e_step(count, name = "Tweets", step = "middle", legend = FALSE) %>%
e_x_axis(
min = 0,
max = 23,
) %>%
e_axis_labels(x = "Time of Day (UTC)", y = "Tweets") %>%
e_theme("westeros") %>%
e_tooltip(trigger = "axis", formatter = htmlwidgets::JS("
function(params) {
let title = `${params[0].value[0]}h`
let num = `${params[0].value[1]} tweets`
return(`${title}${num}`);
}"))
```
Row
-----------------------------------------------------------------------
### 💗 Most Liked Tweet Today {.tweet-box}
```{r most_liked}
most_liked_url <- tweets_today %>%
slice_max(favorite_count)
get_tweet_embed(most_liked_url$screen_name, most_liked_url$status_id)
```
### ✨ Most Retweeted Tweet Today {.tweet-box}
```{r most_rt}
most_retweeted <- tweets_today %>%
slice_max(retweet_count)
get_tweet_embed(most_retweeted$screen_name, most_retweeted$status_id)
```
### 🎉 Most Recent {.tweet-box}
```{r most_recent}
most_recent <- tweets_today %>%
slice_max(created_at, with_ties=FALSE)
get_tweet_embed(most_recent$screen_name, most_recent$status_id)
```
Rankings
=========
Row
-----------------------------------------------------------------------
### Top Tweeters
```{r top_tweeters}
top_tweeters_format %>%
knitr::kable(
format = "html",
escape = FALSE,
align = "cll",
col.names = c("User", "Engagement/Tweet "),
table.attr = 'class = "table"'
)
```
Where Engagement is `RT * 2 + Favourite`
### Network of top tweeters
Relationships in the graph describe replies and quote retweets from the top tweeters
that also have the hashtag.
```{r top_tweeters_net}
edgelist <-
network_data(rstats_tweets %>% unflatten(), "reply,quote")
nodelist <- attr(edgelist, "idsn") %>%
bind_cols()
top_edges <- edgelist %>%
filter((from %in% top_tweeters$user_id) |
(to %in% top_tweeters$user_id))
top_nodes <- nodelist %>%
filter((id %in% top_edges$from) | (id %in% top_edges$to)) %>%
mutate(is_top = ifelse((id %in% top_tweeters$user_id), "yes", "no"),
size = 10)
e_charts() %>%
e_graph() %>%
e_graph_nodes(top_nodes, id, sn, size, category = is_top, legend = FALSE) %>%
e_graph_edges(top_edges, from, to) %>%
e_tooltip()
```
Row
-----------------------------------------------------------------------
### Top Words
```{r top_words}
top_words %>%
knitr::kable(
format = "html",
escape = FALSE,
align = "cll",
col.names = c("Word", "Count"),
table.attr = 'class = "table"'
)
```
Row
-----------------------------------------------------------------------
### Top Hashtags
```{r top_hashtags}
top_hashtags %>%
knitr::kable(
format = "html",
escape = FALSE,
align = "cll",
col.names = c("Hashtag", "Count"),
table.attr = 'class = "table"'
)
```
Excluding `#rstats` and similar variations
### Common co-occuring hashtags
Hashtags that occur together, grouped by community detection
```{r co_hashtags}
top_co_hash_nodes <- tibble(
nodes = c(top_co_hashtags$word1, top_co_hashtags$word2)
) %>%
distinct()
e_chart() %>%
e_graph() %>%
e_graph_nodes(top_co_hash_nodes, nodes, nodes, nodes) %>%
e_graph_edges(top_co_hashtags, word1, word2) %>%
e_modularity()
```
Data
==============
### Tweets in the current week {.datatable-container}
```{r datatable}
tweets_week %>%
select(
status_url,
created_at,
screen_name,
text,
retweet_count,
favorite_count,
mentions_screen_name
) %>%
mutate(
status_url = stringr::str_glue("On Twitter")
) %>%
datatable(
.,
extensions = "Buttons",
rownames = FALSE,
escape = FALSE,
colnames = c("Timestamp", "User", "Tweet", "RT", "Fav", "Mentioned"),
filter = 'top',
options = list(
columnDefs = list(list(
targets = 0, searchable = FALSE
)),
lengthMenu = c(5, 10, 25, 50, 100),
pageLength = 10,
scrollY = 600,
scroller = TRUE,
dom = '<"d-flex justify-content-between"lBf>rtip',
buttons = list('copy', list(
extend = 'collection',
buttons = c('csv', 'excel'),
text = 'Download'
))
)
)
```